8,099 research outputs found
3D Object Discovery and Modeling Using Single RGB-D Images Containing Multiple Object Instances
Unsupervised object modeling is important in robotics, especially for
handling a large set of objects. We present a method for unsupervised 3D object
discovery, reconstruction, and localization that exploits multiple instances of
an identical object contained in a single RGB-D image. The proposed method does
not rely on segmentation, scene knowledge, or user input, and thus is easily
scalable. Our method aims to find recurrent patterns in a single RGB-D image by
utilizing appearance and geometry of the salient regions. We extract keypoints
and match them in pairs based on their descriptors. We then generate triplets
of the keypoints matching with each other using several geometric criteria to
minimize false matches. The relative poses of the matched triplets are computed
and clustered to discover sets of triplet pairs with similar relative poses.
Triplets belonging to the same set are likely to belong to the same object and
are used to construct an initial object model. Detection of remaining instances
with the initial object model using RANSAC allows to further expand and refine
the model. The automatically generated object models are both compact and
descriptive. We show quantitative and qualitative results on RGB-D images with
various objects including some from the Amazon Picking Challenge. We also
demonstrate the use of our method in an object picking scenario with a robotic
arm
QUOTUS: The Structure of Political Media Coverage as Revealed by Quoting Patterns
Given the extremely large pool of events and stories available, media outlets
need to focus on a subset of issues and aspects to convey to their audience.
Outlets are often accused of exhibiting a systematic bias in this selection
process, with different outlets portraying different versions of reality.
However, in the absence of objective measures and empirical evidence, the
direction and extent of systematicity remains widely disputed.
In this paper we propose a framework based on quoting patterns for
quantifying and characterizing the degree to which media outlets exhibit
systematic bias. We apply this framework to a massive dataset of news articles
spanning the six years of Obama's presidency and all of his speeches, and
reveal that a systematic pattern does indeed emerge from the outlet's quoting
behavior. Moreover, we show that this pattern can be successfully exploited in
an unsupervised prediction setting, to determine which new quotes an outlet
will select to broadcast. By encoding bias patterns in a low-rank space we
provide an analysis of the structure of political media coverage. This reveals
a latent media bias space that aligns surprisingly well with political ideology
and outlet type. A linguistic analysis exposes striking differences across
these latent dimensions, showing how the different types of media outlets
portray different realities even when reporting on the same events. For
example, outlets mapped to the mainstream conservative side of the latent space
focus on quotes that portray a presidential persona disproportionately
characterized by negativity.Comment: To appear in the Proceedings of WWW 2015. 11pp, 10 fig. Interactive
visualization, data, and other info available at
http://snap.stanford.edu/quotus
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